Neural Network - PowerPoint PPT Presentation

Neural networks offer modelling procedures for nonlinear data, which enables you to discover more complex relationships in your data. You can thus develop more accurate and effective predictive models.

Copyright Complaint Adult Content Flag as Inappropriate

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

Download Presentation

PowerPoint Slideshow about ' Neural Network' - cherie

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

The MLP neural network algorithm is based on the functional principles of biological neural structures, as indicated in the figure (below).

Multilayer Perceptron Neural Network Algorithm (MLP)

Neural computing researches attempt to organise mathematical models similarly to the structures and organization of neurons in biological brains to achieve similar processing abilities, in addition to the inherent capacities of biological brains, such as learning based on examples, trial and error, knowledge generalization, among many others.

Multilayer Perceptron Neural Network Algorithm (MLP)

Biological Neural Structure models similarly to the structures and organization of neurons in biological brains to achieve similar processing abilities, in addition to the inherent capacities of biological brains, such as learning based on examples, trial and error, knowledge generalization, among many others.

Based on this analogy to biological neurons, the MLP neural network algorithm implements a neural network that is composed of layers of artificial neurons that are stimulated by input signals, which are transmitted through the network via synapses connecting neurons in different layers. The figure presents an artificial perceptron neuron model with n inputs {x1, x2, …, xn}, in which each input xi has an associated synapse wi, and an output y.

Multilayer Perceptron Neural Network Algorithm (MLP)

Artificial Neuron Model network algorithm implements a neural network that is composed of layers of artificial neurons that are stimulated by input signals, which are transmitted through the network via synapses connecting neurons in different layers. The figure presents an artificial perceptron neuron model with n inputs {x

There is also an additional neuron parameter, named w network algorithm implements a neural network that is composed of layers of artificial neurons that are stimulated by input signals, which are transmitted through the network via synapses connecting neurons in different layers. The figure presents an artificial perceptron neuron model with n inputs {x0, known as bias that can be interpreted as a synapse associated to an input x0 = -1. The output of the neuron y is based on the product between input vector x (x0, x1, x2, …, xn) and vector w (w0,w1, w2, …, wn) composed of synapses, including the bias (w0),

Multilayer Perceptron Neural Network Algorithm (MLP)

The artificial neuron model is feed forward, that is, connections are directed from inputs (x0, x1, …, xn) to output y of the neuron. The figure presents the layout of perceptron neurons in an MLP neural network, in which there are two neuron layers, one hidden and one output. Regarding the neural network that is presented in the figure, each neuron of the hidden layer is connected to each neuron in the output layer. Therefore, inputs of the output layer neurons correspond to the outputs of hidden layer neurons.

Multilayer Perceptron Neural Network Algorithm (MLP)

The analyst that uses the artificial neural network algorithm must choose how many neurons to use in the hidden layer, considering the set of input data, since with a low number of neurons in the hidden layer; the neural network is not able to generalize each class's data. However, a high number of neurons in the hidden layer prompts the over fitting phenomenon, in which the neural network exclusively learns training data, and does not generalize learning for data classes.

Multilayer Perceptron Neural Network Algorithm (MLP)

Example Of A Neural Network With A Hidden Layer algorithm must choose how many neurons to use in the hidden layer, considering the set of input data, since with a low number of neurons in the hidden layer; the neural network is not able to generalize each class's data. However, a high number of neurons in the hidden layer prompts the over fitting phenomenon, in which the neural network exclusively learns training data, and does not generalize learning for data classes.

The neural network training process is conducted based on a back propagation algorithm, with the purpose of adjusting values that are associated to synapses to allow the neural network to map an input space and output space, in which the input vectors x are samples of the input space and each input vector is associated to an output z, which can be represented by a vector z (z1, z2, …, zn), based on a scalable value or a symbolic value. Specifically, for symbolic values, a neuron in the output layer corresponds to each of the possible symbols that are associated to the input vector.

Multilayer Perceptron Neural Network Algorithm (MLP)

During the neural network training process, a set of input data is initially determined, to which the associated output is known, and random values are attributed to each synapse in the neural network. The data is presented to the neural network and the supplied output is compared to the actual output, generating an error value. The error value is then employed to adjust reverse neural network synapses, from output to inputs (back propagated).

Multilayer Perceptron Neural Network Algorithm (MLP)

The process of adjusting synapse values is repeated until an interruption criterion is established, for example, a fixed number of repetitions or a minimum error. Thus, in each repetition, the outputs that are provided by the neural network get closer to the actual output. The synapse value correction equations minimize errors between the output that is provided by the neural network and the actual output.

Multilayer Perceptron Neural Network Algorithm (MLP)

For conventional regression, the methodology employed consists in using neurons with linear activation function and assigning an output neuron to map each of the output vector's components. In cases where the output is the only scalable value, the neural network is designed with a single neuron in the output layer.

Two examples are considered.

Multilayer Perceptron Neural Network Algorithm (MLP)

For conventional regression, the methodology employed consists in using neurons with linear activation function and assigning an output neuron to map each of the output vector's components. In cases where the output is the only scalable value, the neural network is designed with a single neuron in the output layer.

Two examples are considered.

Multilayer Perceptron Neural Network Algorithm (MLP)

This data set was downloaded from consists in using neurons with linear activation function and assigning an output neuron to map each of the output vector's components. In cases where the output is the only scalable value, the neural network is designed with a single neuron in the output layer.archive or more directly example.

Data Set A

It describes variables affecting concrete compressive strength. Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, super-plasticizer, coarse aggregate, and fine aggregate.

Data Set A

It describes variables affecting concrete compressive strength. Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, super-plasticizer, coarse aggregate, and fine aggregate.

Data Set A

The file that is used is organised in nine columns, in which each line represents data that is collected from a concrete mixture analysed in a lab. The first seven columns correspond to data about concentration of elements in the mixture, in kg by m3 of concrete; the following column corresponds to the age of the concrete, in days; and the last column corresponds to the sturdiness of the concrete, which is measured in MPa (mega Pascal, pressure measurement unit).

Data Set A

Variable Information each line represents data that is collected from a concrete mixture analysed in a lab. The first seven columns correspond to data about concentration of elements in the mixture, in kg by m3 of concrete; the following column corresponds to the age of the concrete, in days; and the last column corresponds to the sturdiness of the concrete, which is measured in MPa (mega Pascal, pressure measurement unit).

All of these attributes are numerical variables whose values correspond to the measurement unit; thus, the neural network that is used is designed to solve a regression type problem in which the input space comprises the first eight columns of the file (cement concentration to age) and the output space corresponds to the ninth column of the file (concrete sturdiness).

Data Set A

What Does It Look Like? correspond to the measurement unit; thus, the neural network that is used is designed to solve a regression type problem in which the input space comprises the first eight columns of the file (cement concentration to age) and the output space corresponds to the ninth column of the file (concrete sturdiness).

SPSS Commands correspond to the measurement unit; thus, the neural network that is used is designed to solve a regression type problem in which the input space comprises the first eight columns of the file (cement concentration to age) and the output space corresponds to the ninth column of the file (concrete sturdiness).

SPSS Commands correspond to the measurement unit; thus, the neural network that is used is designed to solve a regression type problem in which the input space comprises the first eight columns of the file (cement concentration to age) and the output space corresponds to the ninth column of the file (concrete sturdiness).

Note that the covariates have been standardised, so that none are numerically dominant.

SPSS Commands correspond to the measurement unit; thus, the neural network that is used is designed to solve a regression type problem in which the input space comprises the first eight columns of the file (cement concentration to age) and the output space corresponds to the ninth column of the file (concrete sturdiness).

As a first trial a small hidden layer is adopted, this may be increased in succeeding applications of the procedure.

SPSS Commands correspond to the measurement unit; thus, the neural network that is used is designed to solve a regression type problem in which the input space comprises the first eight columns of the file (cement concentration to age) and the output space corresponds to the ninth column of the file (concrete sturdiness).

SPSS Commands correspond to the measurement unit; thus, the neural network that is used is designed to solve a regression type problem in which the input space comprises the first eight columns of the file (cement concentration to age) and the output space corresponds to the ninth column of the file (concrete sturdiness).

The outputs are saved so that further analysis may be undertaken.

Neural Network With Optimal Architecture correspond to the measurement unit; thus, the neural network that is used is designed to solve a regression type problem in which the input space comprises the first eight columns of the file (cement concentration to age) and the output space corresponds to the ninth column of the file (concrete sturdiness).

As previously mentioned, each neural network input element has an associated synapse, which is represented by a numerical value that controls input; the higher the synapse value, the more relevant is the input for the result that is generated by the neural network.

The figure presents a relevance chart for each input field to obtain fitting results, information that becomes available after the creation of the node corresponding to the model generated.

Metrics are applied to the results represent the linear correlation coefficient, which is based on the equation and absolute average error. Which is based on the equation for both generic data sets, in which the correlation coefficient measures the influence between data sets, and can also be interpreted as a coefficient of similarity between two data sets; and the absolute average error measures inconsistencies between two data sets..

Additional Analysis

Additional Analysis correlation coefficient, which is based on the equation and absolute average error. Which is based on the equation for both generic data sets, in which the correlation coefficient measures the influence between data sets, and can also be interpreted as a coefficient of similarity between two data sets; and the absolute average error measures inconsistencies between two data sets..

Additional Analysis correlation coefficient, which is based on the equation and absolute average error. Which is based on the equation for both generic data sets, in which the correlation coefficient measures the influence between data sets, and can also be interpreted as a coefficient of similarity between two data sets; and the absolute average error measures inconsistencies between two data sets..

Also

Additional Analysis correlation coefficient, which is based on the equation and absolute average error. Which is based on the equation for both generic data sets, in which the correlation coefficient measures the influence between data sets, and can also be interpreted as a coefficient of similarity between two data sets; and the absolute average error measures inconsistencies between two data sets..

Additional Analysis correlation coefficient, which is based on the equation and absolute average error. Which is based on the equation for both generic data sets, in which the correlation coefficient measures the influence between data sets, and can also be interpreted as a coefficient of similarity between two data sets; and the absolute average error measures inconsistencies between two data sets..

Additional Analysis correlation coefficient, which is based on the equation and absolute average error. Which is based on the equation for both generic data sets, in which the correlation coefficient measures the influence between data sets, and can also be interpreted as a coefficient of similarity between two data sets; and the absolute average error measures inconsistencies between two data sets..

Additional Analysis correlation coefficient, which is based on the equation and absolute average error. Which is based on the equation for both generic data sets, in which the correlation coefficient measures the influence between data sets, and can also be interpreted as a coefficient of similarity between two data sets; and the absolute average error measures inconsistencies between two data sets..

Additional Analysis correlation coefficient, which is based on the equation and absolute average error. Which is based on the equation for both generic data sets, in which the correlation coefficient measures the influence between data sets, and can also be interpreted as a coefficient of similarity between two data sets; and the absolute average error measures inconsistencies between two data sets..

Syntax

Summary

Summary so values will only be broadly similar with those presented here).

By analysing results that are obtained by the neural network with two, four, and six hidden layer neurons, it is concluded that the best neural network configuration is probably four hidden neurons, which presents both a higher correlation and decreased error between actual data and estimated values. The goal is to be parsimonious, combining a good fit with as few neurons as possible.

There are 16 instances in Groups 1 to 6 that contain a single missing (i.e., unavailable) attribute value.

Class distribution Benign: 458 (65.5%) Malignant: 241 (34.5%).

Data Set B

Attribute Information clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself:

What Does It Look Like? clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself:

SPSS Commands For Additional Analysis clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself:

SPSS Commands For Additional Analysis clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself:

As previously mentioned, each neural network input element has an associated synapse, which is represented by a numerical value that controls input; the higher the synapse value, the more relevant is the input for the result that is generated by the neural network.

The decision of which algorithm is most appropriate, in this case, falls to a medical field expert, since only such qualified professional is able to assess which error would entail greater damages to patients, considering that a benign tumour erroneously identified as a malign tumour might cause psychological damages to patients, and most malign tumour treatments have severe side effects, while an erroneous malign tumour diagnosis, identified as a benign tumour, might delay treatment, causing the patient to lose valuable time in his/her recovery process

Data Set B

An application with the goal of assisting medical diagnosis of cancer diseases, identifying whether patients have malign or benign cancer. Considering the greatest average success rates, as an alternative base for a complete diagnosis support system, which can be implemented in hospitals, medical clinics, or any other health care institutions, thus reducing the probability of incorrect diagnosis.